Cloud Engineer
On-siteAustin, Texas, United States
Job Summary
Build and maintain the secure cloud platform powering the Army's Data Science Environment by designing architectures in Microsoft Azure and Azure Government. Automate infrastructure and CI/CD pipelines using Terraform, Bicep, and ARM templates while deploying containerized workloads with Docker and Kubernetes. Implement DevSecOps practices, including security scanning, compliance validation, and automated testing, alongside monitoring and observability solutions. Collaborate with data scientists, developers, and cybersecurity teams to enable rapid deployment of AI/ML models and advanced analytics tools. Support operational reliability, incident response, and platform performance tuning. Requires a Bachelor's degree, three years of cloud engineering experience, and an Active Security clearance.
Required Qualifications
- Bachelor's degree in Computer Science, Information Systems, or related technical discipline
- 3+ years of experience in cloud engineering, infrastructure engineering, or DevOps roles
- Experience designing and deploying solutions in Microsoft Azure
- Experience implementing Infrastructure as Code and automated deployment pipelines
- Familiarity with containerization technologies such as Docker or Kubernetes
- Knowledge of secure cloud architecture and cybersecurity best practices
- Experience working in Agile development environments
- Active Security clearance required
- Microsoft Azure / Azure Government
- Infrastructure as Code (Terraform, ARM, Bicep)
- Kubernetes and container platforms
- CI/CD tools (GitLab, GitHub, Azure DevOps)
- Python or PowerShell automation
- Monitoring and observability platforms such as Azure Monitor, Prometheus, and Grafana
- Identity and access management using Azure Active Directory
Desired Qualifications
- Microsoft Azure certifications (AZ-104, AZ-305, or similar)
- Experience working in Azure Government or other DoD cloud environments
- Familiarity with DoD cybersecurity frameworks such as RMF and STIG implementation
- Experience supporting enterprise data platforms, analytics environments, or AI/ML workloads
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